Agachai Sumalee

Reliable Shortest Path Problems in Stochastic Time-Dependent Networks

This study investigates the time-dependent reliable shortest path problem (TD-RSPP), which is commonly encountered in congested urban road networks. Two variants of TD-RSPP are considered in this study. The first variant is to determine the earliest arrival time and associated reliable shortest path for a given departure time, referred to as the “forward” TD-RSPP. The second problem is to determine the latest departure time and associated reliable shortest path for a given preferred arrival time, referred as the “backward” TD-RSPP. It is shown in this article that TD-RSPP is not reversible. The backward TD-RSPP cannot be solved by the algorithms designed for the forward problem using the reverse search from destination to origin. In this study, two efficient solution algorithms are proposed to solve the forward and backward TD-RSPP exactly and the optimality of proposed algorithms is rigorously proved. The proposed solution algorithms have potential applications in both advanced traveler information systems and stochastic dynamic traffic assignment models.

Optimal and robust strategies for freeway traffic management under demand and supply uncertainties: An overview and general theory

This paper investigates optimal decision-making for traffic management under demand and supply uncertainties by stochastic dynamic programming. Traffic flow dynamics under demand and supply uncertainties is described by a simplified version of the stochastic cell transmission model. Based on this model, the optimal traffic management problem is analysed wherein the existence of solution is guaranteed by verifying the well-posed condition. An analytical optimal control law is derived in terms of a set of coupled generalised recursive Riccati equations. As optimal control laws may be fragile with respect to model misspecification, a robust (optimal) decision-making law that aims to act robust with respect to the parameter misspecification in the traffic flow model (which can be originated from model calibration), and to attenuate the effect of disturbances in freeway networks (wherein demand uncertainty is usually regarded as a kind of disturbance) is proposed. Conventionally, network uncertainties have been considered to induce negative effects on traffic management in transportation literature. In contrast, the proposed methodology outlines an interesting issue that is to make benefit (or trade-off) from the inherent network uncertainties. Finally, some practical issues in traffic management that can be addressed by extending the current framework are briefly discussed.

Modeling the effects of public bicycle schemes in a congested multi-modal road network

With increasing concerns about environmental and energy issues in many large Chinese cities, local authorities are introducing public bicycle schemes to promote the use of green transportation modes. This paper proposes a novel model for investigating the effects of the public bicycle schemes in a congested multi-modal road network with auto, bus, and public bicycle travel. The decision-making process of travelers regarding travel modes and route choices is assumed to follow a hierarchical choice structure. The effects of pollution emissions by motorized vehicles (i.e., autos and buses), crowding discomfort in buses, and riding fatigue on bicycles are considered in the proposed model. The multi-modal travel choice equilibrium problem is formulated as an equivalent variational inequality problem. The existence and uniqueness of the solution of the proposed model are examined. A heuristic solution algorithm that combines a diagonalization approach and the method of successive averages is adapted to solve the proposed model. A numerical example is given to illustrate the application of the proposed model and solution algorithm. Findings are reported on the effects of the public bicycle schemes and emission tax policy on the multi-modal transportation system. The optimal public bicycle rental price and emission tax for maximization of social welfare can also be determined by the proposed model.

Bus lane safety implications: a case study in Hong Kong

Bus lanes have been widely adopted in major cities such as Hong Kong to improve the efficiency of bus services and increase bus modal share. However, their impacts on road safety have been overlooked. This study employs two observational before–after study techniques aiming to examine whether accident occurrences of the roads equipped with bus lanes had been changed thereafter. On the seven studied sites, decrease in public bus accidents, both fatal and serious (FS) and fatal, serious and slight (FSS) were found; but, increase in other vehicle FS accident were found. Albeit only two of the decreases and neither of the increases were statistically significant at the 5% level, the results appear to suggest that only public buses have benefited in terms of road safety from the bus lane operation. Further investigation into the impacts on non-bus traffic is thus required to provide an insight for future bus lane planning regarding safety.

Solving a mixed integer linear program approximation of the toll design problem using constraint generation within a branch-and-cut algorithm

This paper addresses the global optimality of the toll design problem (TDP) by formulating a mixed integer linear program (MILP) approximation. In the TDP, the objective is to maximise the social surplus by adjusting toll locations and levels in a road traffic network. The resulting optimisation problem can be formulated as a mathematical program with equilibrium constraints. An MILP is obtained by piecewise linear approximation of the nonlinear functions in the TDP, and we present a domain reduction scheme to reduce the error introduced by these approximations. Previous approaches for solving the MILP approximation have been relying on a large number of MILPs to be solved iteratively within a cutting constraint algorithm (CCA). This paper instead focuses on the development of a solution algorithm for solving the MILP approximation in which the CCA is integrated within a branch-and-cut algorithm, which only requires one MILP to be solved.

Updating of Travel Behavior Model Parameters and Estimation of Vehicle Trip Chain Based on Plate Scanning

This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases.

Network-wide on-line travel time estimation with inconsistent data from multiple sensor systems under network uncertainty

ABSTRACT This paper proposes a new modeling approach for network-wide on-line travel time estimation with inconsistent data from multiple sensor systems. It makes full use of both the available data from multiple sensor systems (on-line data) and historical data (off-line data). The first- and second-order statistical properties of the on-line data are investigated together with the data inconsistency issue to estimate network-wide travel times. The proposed model is formulated as a generalized least squares problem with non-linear constraints. A solution algorithm based on the penalty function method is adopted to solve the proposed model, whose application is illustrated by numerical examples using a local road network in Hong Kong.

Automatic Freeway Incident Detection for Free Flow Conditions: A Vehicle Reidentification Based Approach Using Image Data from Sparsely Distributed Video Cameras

This paper proposes a vehicle reidentification (VRI) based automatic incident algorithm (AID) for freeway system under free flow condition. An enhanced vehicle feature matching technique is adopted in the VRI component of the proposed system. In this study, arrival time interval, which is estimated based on the historical database, is introduced into the VRI component to improve the matching accuracy and reduce the incident detection time. Also, a screening method, which is based on the ratios of the matching probabilities, is introduced to the VRI component to further reduce false alarm rate. The proposed AID algorithm is tested on a 3.6 km segment of a closed freeway system in Bangkok, Thailand. The results show that in terms of incident detection time, the proposed AID algorithm outperforms the traditional vehicle count approach.

Transport dynamics: its time has come!

The beginning of the twenty-first century and the last decades in the twentieth century have indeed been an exciting era for research and development in traffic and transportation systems. Considerable advances in real-time traffic detection, data processing, communications, and control methods have enabled new frontiers in both developing a deeper understanding of the nature of traffic and transportation as well as opening up new ways to manage and control the transportation system in response to the actual conditions. Steadily, we have been stepping out of the vintage point of viewing traffic and transportation as static entities, and beginning to analyse and manage them as dynamic phenomena and processes as ought to be. It is an opportune time to launch this new journal to provide a platform to help propel this endeavor to new heights, meanwhile archiving significant findings along the way for the future generations to come. Transportmetrica B aims to bring together contributions of advanced research in understanding and practical experience in handling the dynamic aspects of transport systems and behaviour, and hence the sub-title is set as “Transport Dynamics”. Transport dynamics can be considered from various scales and scopes ranging from dynamics in traffic flow, travel behaviour (e.g. learning process), logistics, and transport policy, to traffic control. Thus, the journal welcomes research papers that address transport dynamics from a broad perspective, ranging from theoretical studies to empirical analysis of transport systems or behaviour based on actual data. The scope of Transportmetrica B includes, but is not limited to, the following: dynamic traffic assignment (DTA); dynamic transit assignment; dynamic activity-based modelling; applications of system dynamics in transport planning, logistics planning, and optimisation; traffic flow analysis; dynamic programming in transport modelling and optimisation; traffic control, land-use, and transport dynamics; day-to-day learning process (model and behavioural studies); time-series analysis of transport data and demand; traffic emission modelling; time-dependent transport policy analysis; transportation network reliability and vulnerability; simulation of traffic system and travel behaviour; longitudinal analysis of traveller behaviour; etc. The launch of Transportmetrica B has gained the full support of the Hong Kong Society for Transportation Studies, and we are gratified to have established a very strong group of editors, serving as Consulting Editors, Editors, and Associate Editors, all of whom are world-renowned, top contributors to the various aspects of transport dynamics. Indeed, we are grateful to their willingness and effort to serve on the editorial board of this journal, monitoring the editorial policy of this journal, both in terms of its coverage and the quality of papers, and highlighting new research areas that should be covered by the journal. In this inaugural issue, we are very pleased to include five papers. The paper by Watling and Cantarella, titled “Modelling sources of variation in transportation systems: theoretical foundations of day-to-day dynamic models”, synthesises and advances the theory on epoch-to-epoch adaptive behaviour of travellers, such as the day-to-day dynamics of drivers’ route choices, represented as either a stochastic or deterministic process. The paper examines how moment-based deterministic dynamical systems may be exactly or approximately derived from some underlying

Evolutionary Marginal Cost Pricing Scheme Implementation Based on Stochastic Traffic Flow Information

Traditionally, to implement the first-best marginal cost pricing scheme in a traffic network requires the information on the exact demand function or true origin-destination demand, which, however, is rarely available in practice. To overcome this dilemma, the trial-and-error method has been proposed to find the marginal cost toll through an iterative process using the observed traffic volumes. This method guarantees the convergence of tolls and flows to the system optimal state under the assumption of deterministic traffic conditions. However, in reality, the uncertainty of transportation network has been recognized well that induces the variability of link flow and travel time. Therefore, this paper proposes an evolutionary implementation method that iteratively finds the first-best marginal cost toll pattern according to the observed stochastic link flow information and the known travel time functions. The proof of the convergence of the iterative algorithm is presented. The paper also analyzes the effect of the sampling error of the link flow data on the convergence of the algorithm and shows that the biases from the flow observation will not affect the convergence. The numerical tests are provided for the illustration of the algorithm.